{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "一、数据说明： Capital Bikeshare （美国Washington, D.C.的一个共享单车公司）提供的共享单车数据。\n",
    "数据包含每天的日期、天气等信息，需要预测每天的共享单车骑行量。\n",
    "1. 对数据做数据探索分析（可参考EDA_BikeSharing.ipynb，不计分）\n",
    "2. 适当的特征工程（可参考FE_BikeSharing.ipynb，不计分）\n",
    "3. 对全体数据，随机选择其中80%做训练数据，剩下20%为测试数据，评价指标为RMSE。（10分）\n",
    "4. 用训练数据训练最小二乘线性回归模型（20分）、岭回归模型、Lasso模型，其中岭回归模型（30分）和Lasso模型（30分），注意岭回归模型和Lasso模型的正则超参数调优。\n",
    "5. 比较用上述三种模型得到的各特征的系数，以及各模型在测试集上的性能。并简单说明原因。（10分）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 共享单车分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.344167</td>\n",
       "      <td>0.363625</td>\n",
       "      <td>0.805833</td>\n",
       "      <td>0.160446</td>\n",
       "      <td>331</td>\n",
       "      <td>654</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.363478</td>\n",
       "      <td>0.353739</td>\n",
       "      <td>0.696087</td>\n",
       "      <td>0.248539</td>\n",
       "      <td>131</td>\n",
       "      <td>670</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-03</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.196364</td>\n",
       "      <td>0.189405</td>\n",
       "      <td>0.437273</td>\n",
       "      <td>0.248309</td>\n",
       "      <td>120</td>\n",
       "      <td>1229</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-04</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>0.160296</td>\n",
       "      <td>108</td>\n",
       "      <td>1454</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-05</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>82</td>\n",
       "      <td>1518</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
       "0        1  2011-01-01       1   0     1        0        6           0   \n",
       "1        2  2011-01-02       1   0     1        0        0           0   \n",
       "2        3  2011-01-03       1   0     1        0        1           1   \n",
       "3        4  2011-01-04       1   0     1        0        2           1   \n",
       "4        5  2011-01-05       1   0     1        0        3           1   \n",
       "\n",
       "   weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
       "0           2  0.344167  0.363625  0.805833   0.160446     331         654   \n",
       "1           2  0.363478  0.353739  0.696087   0.248539     131         670   \n",
       "2           1  0.196364  0.189405  0.437273   0.248309     120        1229   \n",
       "3           1  0.200000  0.212122  0.590435   0.160296     108        1454   \n",
       "4           1  0.226957  0.229270  0.436957   0.186900      82        1518   \n",
       "\n",
       "    cnt  \n",
       "0   985  \n",
       "1   801  \n",
       "2  1349  \n",
       "3  1562  \n",
       "4  1600  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sn\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "#读入数据\n",
    "train = pd.read_csv(\"day.csv\")\n",
    "train.head()                   \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据探索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ec32328310>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sn.violinplot(data=train[['season',\n",
    "                        'cnt']],\n",
    "              x=\"season\",y=\"cnt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'dayly distribution of counts')]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import datetime\n",
    "\n",
    "train['date'] = pd.to_datetime(train['dteday'])\n",
    "train['dayofyear'] = train[\"date\"].dt.dayofyear  #减今年的第几天\n",
    "\n",
    "fig,ax = plt.subplots()\n",
    "sn.pointplot(data=train[['dayofyear',\n",
    "                           'cnt',\n",
    "                           'yr']],\n",
    "             x='dayofyear',y='cnt',\n",
    "             hue='yr',ax=ax)\n",
    "ax.set(title=\"dayly distribution of counts\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ec34fe97c0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#相关性\n",
    "corrMatt = train[[\"temp\",\"atemp\",\n",
    "                  \"hum\",\"windspeed\",\n",
    "                  \"casual\",\"registered\",\n",
    "                  \"cnt\"]].corr()\n",
    "mask = np.array(corrMatt)\n",
    "mask[np.tril_indices_from(mask)] = False\n",
    "sn.heatmap(corrMatt, mask=mask,\n",
    "           vmax=.8, square=True,annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 特征工程"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 731 entries, 0 to 730\n",
      "Data columns (total 35 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   instant       731 non-null    int64  \n",
      " 1   season_1      731 non-null    uint8  \n",
      " 2   season_2      731 non-null    uint8  \n",
      " 3   season_3      731 non-null    uint8  \n",
      " 4   season_4      731 non-null    uint8  \n",
      " 5   mnth_1        731 non-null    uint8  \n",
      " 6   mnth_2        731 non-null    uint8  \n",
      " 7   mnth_3        731 non-null    uint8  \n",
      " 8   mnth_4        731 non-null    uint8  \n",
      " 9   mnth_5        731 non-null    uint8  \n",
      " 10  mnth_6        731 non-null    uint8  \n",
      " 11  mnth_7        731 non-null    uint8  \n",
      " 12  mnth_8        731 non-null    uint8  \n",
      " 13  mnth_9        731 non-null    uint8  \n",
      " 14  mnth_10       731 non-null    uint8  \n",
      " 15  mnth_11       731 non-null    uint8  \n",
      " 16  mnth_12       731 non-null    uint8  \n",
      " 17  weathersit_1  731 non-null    uint8  \n",
      " 18  weathersit_2  731 non-null    uint8  \n",
      " 19  weathersit_3  731 non-null    uint8  \n",
      " 20  weekday_0     731 non-null    uint8  \n",
      " 21  weekday_1     731 non-null    uint8  \n",
      " 22  weekday_2     731 non-null    uint8  \n",
      " 23  weekday_3     731 non-null    uint8  \n",
      " 24  weekday_4     731 non-null    uint8  \n",
      " 25  weekday_5     731 non-null    uint8  \n",
      " 26  weekday_6     731 non-null    uint8  \n",
      " 27  temp          731 non-null    float64\n",
      " 28  atemp         731 non-null    float64\n",
      " 29  hum           731 non-null    float64\n",
      " 30  windspeed     731 non-null    float64\n",
      " 31  holiday       731 non-null    int64  \n",
      " 32  workingday    731 non-null    int64  \n",
      " 33  yr            731 non-null    int64  \n",
      " 34  cnt           731 non-null    int64  \n",
      "dtypes: float64(4), int64(5), uint8(26)\n",
      "memory usage: 70.1 KB\n"
     ]
    }
   ],
   "source": [
    "#对类别型特征，观察其取值范围及直方图\n",
    "categorical_features = ['season','mnth','weathersit','weekday']\n",
    "\n",
    "#数据类型变为object，才能被get_dummies处理\n",
    "for col in categorical_features:\n",
    "    train[col] = train[col].astype('object')\n",
    "    \n",
    "X_train_cat = train[categorical_features]\n",
    "X_train_cat = pd.get_dummies(X_train_cat)\n",
    "X_train_cat.head()\n",
    "\n",
    "#数值型变量预处理，\n",
    "#感觉数据已经做过处理（取值都在0-1之间），这里用MinMaxScaler再处理一次\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "mn_X = MinMaxScaler()\n",
    "numerical_features = ['temp','atemp','hum','windspeed']\n",
    "temp = mn_X.fit_transform(train[numerical_features])\n",
    "\n",
    "X_train_num = pd.DataFrame(data=temp, columns=numerical_features, index =train.index)\n",
    "X_train_num.head()\n",
    "\n",
    "# Join categorical and numerical features\n",
    "X_train = pd.concat([X_train_cat, X_train_num, train['holiday'],  train['workingday']], axis = 1, ignore_index=False)\n",
    "\n",
    "FE_train = pd.concat([train['instant'], X_train,  train['yr'],train['cnt']], axis = 1)\n",
    "FE_train.to_csv('FE_day.csv', index=False)\n",
    "FE_train.head()\n",
    "FE_train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 对全体数据，随机选择其中80%做训练数据，剩下20%为测试数据，评价指标为RMSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#训练最小二乘线性回归、岭回归、和Lasso模型\n",
    "#读取数据\n",
    "dg = pd.read_csv(\"FE_day.csv\")\n",
    "# 从原始数据中分离输入特征x和输出y\n",
    "y = dg[\"cnt\"]\n",
    "X = dg.drop([\"cnt\"], axis = 1)\n",
    "#特征名称，用于后续显示权重系数对应的特征\n",
    "feat_names = X.columns\n",
    "#将数据分割训练数据与测试数据\n",
    "from sklearn.model_selection import train_test_split\n",
    "# 随机采样20%的数据构建测试样本，其余作为训练样本\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=30, test_size=0.2)\n",
    "\n",
    "from math import sqrt\n",
    "\n",
    "def root_mean_squared_error(y_true,y_predict):\n",
    "    \"\"\"计算y_true和y_predict之间的RMES\"\"\"\n",
    "    assert len(y_true) == len(y_predict), \\\n",
    "        \"the size of y_true must be equal to be the size of y_predict\"\n",
    "    return sqrt(np.sum((y_true - y_predict) ** 2 ) / len(y_true))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 用训练数据训练最小二乘线性回归模型、岭回归模型、Lasso模型，注意岭回归模型和Lasso模型的正则超参数调优"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear is [   -8.8071078   -841.38159807   154.61007532   -27.30093218\n",
      "   714.07245493 -1794.63009181 -1411.41668011  -770.90688579\n",
      "  -708.29638183  -141.70611047    51.3269637   -127.02430064\n",
      "   609.0776189   1434.39190672  1092.41755789   742.72138713\n",
      "  1024.0450163    772.83206902   357.76084035 -1130.59290938\n",
      "  -126.1090729   -168.28995083   -33.9359571     -6.89571982\n",
      "    23.20227078   -29.98286478   342.01129465 -1317.43113044\n",
      "  5049.13480358 -1425.865239   -1263.56809046  -371.10566394\n",
      "   155.20344219  5223.88835304]\n",
      "The RMSE score of LinearRegression on test is 799.442818437365\n",
      "The RMSE score of LinearRegression on train is 754.3457349210204\n",
      "L2 is [ 1.01489355e+00 -8.53217245e+02  1.64156188e+02 -1.00810318e+01\n",
      "  6.99142089e+02 -2.10337527e+02 -9.40446039e+01  2.59741111e+02\n",
      "  4.79874904e+01  3.17589767e+02  2.22387895e+02 -2.15896052e+02\n",
      "  1.66736204e+02  6.57985794e+02  6.66539386e+01 -5.85686527e+02\n",
      " -6.33117490e+02  7.94052993e+02  3.44512825e+02 -1.13856582e+03\n",
      " -1.27649034e+02 -1.69787566e+02 -3.32209227e+01 -9.50383010e+00\n",
      "  2.81116035e+01 -2.27324332e+01  3.34782182e+02  1.42445455e+03\n",
      "  1.89416941e+03 -1.18560121e+03 -1.27133916e+03 -3.64945362e+02\n",
      "  1.57812213e+02  1.63579482e+03]\n",
      "L2 alphas is 1.0\n",
      "The RMSE score of RidgeCV on test is 753.2926497416528\n",
      "The RMSE score of RidgeCV on train is 762.2176378821304\n",
      "L1 is [   5.49834712 -381.02004708    0.            0.           -0.\n",
      "   -0.           -0.           -0.            0.            0.\n",
      "    0.            0.            0.            0.            0.\n",
      "   -0.           -0.            0.           -0.           -0.\n",
      "   -0.           -0.            0.           -0.            0.\n",
      "    0.            0.            0.            0.           -0.\n",
      "   -0.           -0.            0.            0.        ]\n",
      "L1 alphas is 264.5594620795411\n",
      "The RMSE score of LassoCV on test is 1427.0340665384344\n",
      "The RMSE score of LassoCV on train is 1430.9146528875833\n"
     ]
    }
   ],
   "source": [
    "# 线性回归\n",
    "#class sklearn.linear_model.LinearRegression(fit_intercept=True, normalize=False, copy_X=True, n_jobs=1)\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "# 1.使用默认配置初始化学习器实例\n",
    "lr = LinearRegression()\n",
    "\n",
    "# 2.用训练数据训练模型参数\n",
    "lr.fit(X_train, y_train)\n",
    "\n",
    "# 3. 用训练好的模型对测试集进行预测\n",
    "y_test_pred_lr = lr.predict(X_test)\n",
    "y_train_pred_lr = lr.predict(X_train)\n",
    "print(\"Linear is\",lr.coef_)\n",
    "#测试集\n",
    "print ('The RMSE score of LinearRegression on test is', root_mean_squared_error(y_test, y_test_pred_lr))\n",
    "#训练集\n",
    "print ('The RMSE score of LinearRegression on train is', root_mean_squared_error(y_train, y_train_pred_lr))\n",
    "\n",
    "#岭回归／L2正则\n",
    "from sklearn.linear_model import  RidgeCV\n",
    "#1. 设置超参数（正则参数）范围\n",
    "alphas = np.logspace(-100,100,5)\n",
    "#2. 生成一个RidgeCV实例\n",
    "ridge = RidgeCV(alphas=alphas, store_cv_values=True)  \n",
    "#3. 模型训练\n",
    "ridge.fit(X_train, y_train)\n",
    "\n",
    "#4. 预测\n",
    "y_test_pred_ridge = ridge.predict(X_test)\n",
    "y_train_pred_ridge = ridge.predict(X_train)\n",
    "\n",
    "from sklearn.metrics import r2_score\n",
    "# 评估，使用r2_score评价模型在测试集和训练集上的性能\n",
    "print(\"L2 is\",ridge.coef_)\n",
    "print('L2 alphas is',ridge.alpha_)\n",
    "print ('The RMSE score of RidgeCV on test is', root_mean_squared_error(y_test, y_test_pred_ridge))\n",
    "print ('The RMSE score of RidgeCV on train is', root_mean_squared_error(y_train, y_train_pred_ridge))\n",
    "\n",
    "#### Lasso／L1正则\n",
    "from sklearn.linear_model import LassoCV\n",
    "#超参数\n",
    "alphas_2 = np.logspace(-100,100,5)\n",
    "#生成LassoCV实例（默认超参数搜索范围）\n",
    "lasso = LassoCV()  \n",
    "\n",
    "#3. 训练（内含CV）\n",
    "lasso.fit(X_train, y_train)  \n",
    "\n",
    "#4. 测试\n",
    "y_test_pred_lasso = lasso.predict(X_test)\n",
    "y_train_pred_lasso = lasso.predict(X_train)\n",
    "\n",
    "\n",
    "# 评估，使用r2_score评价模型在测试集和训练集上的性能\n",
    "print(\"L1 is\",lasso.coef_)\n",
    "print('L1 alphas is',lasso.alpha_)\n",
    "print ('The RMSE score of LassoCV on test is', root_mean_squared_error(y_test, y_test_pred_lasso))\n",
    "print ('The RMSE score of LassoCV on train is', root_mean_squared_error(y_train, y_train_pred_lasso))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 比较用上述三种模型得到的各特征的系数，以及各模型在测试集上的性能"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从三模型的各特征系数上可以看出，Lasso重点关注了相关度高的特征值，忽略了其他特征的影响，而岭回归则是在最小二乘的基础上对特征的影响进行了抑制，降低各特征系数差距，从而防止过拟合。\n",
    "从各模型的性能比较也可以看出，岭回归拟合最好，同时对于非稀疏特征数据，Lasso拟合的较差。同时在使用80%的数据进行训练时，最小二乘的训练误差小于真实误差，也体现了最小二乘在多维度、大数据下的拟合效果差。"
   ]
  }
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